The present invention relates to an image processing apparatus and an image processing method of enlarging input image information or converting input low-resolution information into high-resolution information.
Conventionally, various methods have been proposed as a method of converting input low-resolution information into high-resolution information. These conventional methods use different conversion processing methods in accordance with the types of images to be processed (e.g., a multivalue image having gradation information in units of pixels, a binary image formed by a pseudo halftone, a binary image formed by a fixed threshold value, and a character image). Examples of generally used conventional interpolation methods are nearest neighbor interpolation by which the same pixel values nearest to an interpolation point are arranged as shown in FIG. 23 and bilinear interpolation by which a pixel value E is determined by the following arithmetic operation in accordance with the distances of four points (having pixel values A, B, C, and D) surrounding an interpolation point as shown in FIG. 24. EQU E=(1-i)(1-j)A+i).multidot.(1-j)B+j.multidot.(1-i)C+ijD
(where i and j are respectively the distances from A in the lateral and longitudinal directions when the interpixel distance is 1. (i.ltoreq.1, j.ltoreq.1))
Unfortunately, the above conventional methods have the following drawbacks.
First, the method shown in FIG. 23 has the advantage that the process configuration is simple. However, when this method is used for natural images, for example, pixel values are determined in units of blocks to be enlarged. Therefore, blocks become visually conspicuous, and this deteriorates the image quality.
Also, even when the method is used for characters, line images, or CG (Computer Graphics) images, the same pixel value continues in units of blocks to be enlarged. Especially when an image contains an oblique line, staircasing called jaggy becomes prominent to deteriorate the image quality. FIGS. 25A and 25B show the way this jaggy occurs. FIG. 25A shows input information, and FIG. 25B shows an example of resolution conversion by which the number of pixels is doubled in both the row and column directions by the method shown in FIG. 23. Generally, the deterioration of image quality increases as the magnification increases. (Referring to FIGS. 25A and 25B, "200" and "10" are pixel values.)
The method shown in FIG. 24 is generally often used in enlarging natural images. In this method, averaged smoothed images are obtained. However, blurred images result when edges or sharp images are required. Additionally, when the method is used for an image obtained by scanning a map or the like or for a natural image containing characters, important information is sometimes not transferred to the user due to a blur caused by interpolation.
FIG. 25C shows image information obtained by interpolation by which the input image information shown in FIG. 25A is doubled in both the row and column directions by the method shown in FIG. 24.
As is apparent from FIG. 25C, the pixel values of not only portions around an oblique line but also the oblique line itself are made nonuniform, and this blurs the image.
Accordingly, a number of techniques of resolution conversion for high image quality have been proposed. For example, U.S. Pat. No. 5,280,546 has disclosed an interpolation method in which in order to remove an interpolation blur, a natural image and a line image are separated, linear interpolation is performed for the natural image, and binary linear interpolation is performed for the line image, thereby arranging the maximum and minimum values of nearest neighbor pixels. In this technique, however, an interpolation blur occurs in natural images. Also, jaggy is inevitably produced because high-resolution information is formed for line images while the resolution of low-resolution information remains unchanged. Additionally, even line images are quantized into two gradation levels. Therefore, although no problem arises when original information has only two gradation levels, the quality of a multi-gradation line image inevitably deteriorates because the number of gradation levels is decreased.
U.S. Pat. No. 5,430,811 has disclosed a nonlinear interpolation technique using an LUT. In this technique, however, the algorithm itself can handle only a magnification of 2.times. in both the horizontal and vertical directions. To increase the magnification, therefore, it is necessary to repeat the processing or combine the processing with another enlarging processing, resulting in complicated processing. In addition, even if a magnification raised to a power of 2 is realized in the repetitive processing, it is not easy to perform control so that each pixel of high-resolution information is given desired nonlinearity at the final magnification when the repetitive processing is taken into consideration. Also, in this technique the pixel values of an observed image (original information) are not changed. Therefore, the production of jaggy is inevitable as in U.S. Pat. No .5,280,546.
U.S. Pat. No. 5,054,100 has also disclosed nonlinear interpolation. This technique is effective for a simple interpolation blur having edges in the row and column directions. However, this effect of the technique cannot be obtained for even slightly complicated image information. Accordingly, jaggy is inevitably produced as in the two previous prior arts.
Furthermore, as resolution conversion of a binary image subjected to pseudo gradation processing such as the dither method or the error diffusion method, the use of techniques which realize high-quality resolution conversion by converting a pixel of interest and its vicinity into a multivalue image through a digital filter, performing linear interpolation, and again binarizing the image is known for long (e.g., U.S. Pat. No. 4,803,558 and Japanese Patent Publication No. 61-56665). Methods which use these techniques to perform high-quality resolution conversion when an original image is a multivalue image have been proposed. However, as long as the quantization technology such as binarization is used for edge formation, it is difficult to form high-quality multivalue images having spatial continuity of pixel values.